export const description =
  'Robyn AI Agents provide intelligent functionality for building AI-powered applications using MCP (Model Context Protocol) implementation with file system access, task management, and context-aware capabilities.'

# Agents

This guide demonstrates how to build AI-powered agents using Robyn's MCP (Model Context Protocol) implementation.

## Overview

The agent system connects AI assistants like Claude Desktop to your development environment, providing seamless access to:

- File system operations (read, search, organize)
- Task and note management
- System monitoring and git integration
- Web content fetching and analysis
- Context-aware code analysis

## Quick Start

1. Run the MCP server:
   ```bash
   python examples/agents.py
   ```

2. Connect your AI assistant to `http://localhost:8080/mcp`

3. Start using natural language commands:
   - "What files are in my projects directory?"
   - "Show me my recent git commits"
   - "Create a note about today's standup meeting"
   - "What processes are using the most CPU?"
   - "Add a task to review the quarterly report"

## Configuration

The assistant creates the following structure:

```
~/Documents/
├── notes/           # Markdown notes
└── tasks.json      # Task list

~/projects/          # Development projects
├── project1/
└── project2/
```

## Security

- File access restricted to home directory
- Safe mathematical expression evaluation
- Path validation for all file operations
- Read-only git operations

## Available Resources

### File System
- `fs://{path}` - Read files in home directory
- `fs://dir/{path}` - List directory contents

### Git Integration
- `git://repo/{repo_name}` - Repository status and commits

### System Monitoring
- `system://processes` - Running processes
- `system://stats` - System statistics

## Available Tools

- `create_note(title, content, tags)` - Create markdown notes
- `add_task(task, priority, due_date)` - Add tasks
- `complete_task(task_id)` - Mark tasks complete
- `search_files(query, directory)` - Search file contents
- `fetch_url_content(url, max_length)` - Download web content

## Available Prompts

- `analyze_file_structure(directory)` - Generate project analysis
- `code_review_request(file_path, focus_area)` - Create code reviews
- `task_prioritization(context)` - Organize and prioritize work

## Dependencies

Optional enhanced functionality:

```bash
pip install psutil  # Enhanced system monitoring
```

## Implementation Examples

### Development Workflow
"Analyze my projects directory and help prioritize work based on recent activity"

### Project Analysis
"Review my web-app project structure and suggest improvements"

### Meeting Notes
"Create a note about today's architecture review with key decisions"

### Code Search
"Find all files mentioning 'authentication' and summarize approaches"

### Task Management
"Add high-priority task to refactor user service, due Friday"

## Integration Benefits

Connecting AI assistants to your development environment enables:
- Native file system browsing
- Context-aware project conversations
- Personalized code suggestions
- Real-time task management
- Workspace-specific code reviews

## Advanced Features

The MCP implementation includes:
- URI templates with parameter extraction
- Auto-generated schemas from type hints
- Async/sync operation handlers
- MCP-compliant error handling
- Type-safe parameter passing

Extend easily with custom resources, tools, and prompts for your specific workflow.